March 2025 monthly summary for apple/axlearn: Focused on performance and correctness improvements in gradient accumulation for faster training. Implemented Efficient Gradient Accumulation and Minibatch Reshaping, fixed minibatch handling, introduced a reshaping method to optimize performance, and added tests to ensure correctness and preserve existing behavior. This work delivered measurable improvements in training throughput and reliability, enabling faster iteration cycles and more robust model training.

February 2025 monthly summary for apple/axlearn focusing on business value through configurability, performance, and hardware integration. Delivered dynamic TRN2 configuration management and hardware partitioning to streamline multi-size model deployments, and Flash Attention optimization for AWS Neuron to accelerate training runs. Implementations include Fuji mesh support, grouped QKV linear layer enhancements, modular partition specs, and a custom configuration generator to streamline setup across model sizes. Also added VJP/backward support and extensive tests for Flash Attention to ensure correctness and portability across configurations.